Overview

Dataset statistics

Number of variables26
Number of observations203
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.8 KiB
Average record size in memory216.0 B

Variable types

Numeric15
Categorical11

Alerts

modèle has a high cardinality: 141 distinct valuesHigh cardinality
etat_de_route is highly overall correlated with empattement and 2 other fieldsHigh correlation
empattement is highly overall correlated with etat_de_route and 11 other fieldsHigh correlation
longueur is highly overall correlated with empattement and 9 other fieldsHigh correlation
largeur is highly overall correlated with empattement and 10 other fieldsHigh correlation
hauteur is highly overall correlated with etat_de_route and 3 other fieldsHigh correlation
poids_vehicule is highly overall correlated with empattement and 8 other fieldsHigh correlation
taille_moteur is highly overall correlated with empattement and 12 other fieldsHigh correlation
taux_alésage is highly overall correlated with empattement and 9 other fieldsHigh correlation
course is highly overall correlated with emplacement_moteur and 1 other fieldsHigh correlation
taux_compression is highly overall correlated with carburant and 3 other fieldsHigh correlation
chevaux is highly overall correlated with empattement and 11 other fieldsHigh correlation
tour_moteur is highly overall correlated with carburantHigh correlation
consommation_ville is highly overall correlated with longueur and 7 other fieldsHigh correlation
consommation_autoroute is highly overall correlated with empattement and 8 other fieldsHigh correlation
prix is highly overall correlated with empattement and 8 other fieldsHigh correlation
carburant is highly overall correlated with taux_compression and 2 other fieldsHigh correlation
turbo is highly overall correlated with taux_compression and 1 other fieldsHigh correlation
nombre_portes is highly overall correlated with etat_de_route and 2 other fieldsHigh correlation
type_vehicule is highly overall correlated with nombre_portesHigh correlation
roues_motrices is highly overall correlated with marqueHigh correlation
emplacement_moteur is highly overall correlated with empattement and 4 other fieldsHigh correlation
type_moteur is highly overall correlated with taille_moteur and 3 other fieldsHigh correlation
nombre_cylindres is highly overall correlated with largeur and 5 other fieldsHigh correlation
systeme_carburant is highly overall correlated with taux_compression and 2 other fieldsHigh correlation
marque is highly overall correlated with empattement and 8 other fieldsHigh correlation
carburant is highly imbalanced (53.6%)Imbalance
emplacement_moteur is highly imbalanced (88.9%)Imbalance
nombre_cylindres is highly imbalanced (57.3%)Imbalance
modèle is uniformly distributedUniform
etat_de_route has 66 (32.5%) zerosZeros

Reproduction

Analysis started2023-04-28 12:15:47.065197
Analysis finished2023-04-28 12:16:11.939999
Duration24.87 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

etat_de_route
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83251232
Minimum-2
Maximum3
Zeros66
Zeros (%)32.5%
Negative25
Negative (%)12.3%
Memory size3.2 KiB
2023-04-28T14:16:11.993217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2473842
Coefficient of variation (CV)1.4983373
Kurtosis-0.67225171
Mean0.83251232
Median Absolute Deviation (MAD)1
Skewness0.2135015
Sum169
Variance1.5559674
MonotonicityNot monotonic
2023-04-28T14:16:12.087415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 66
32.5%
1 54
26.6%
2 31
15.3%
3 27
13.3%
-1 22
 
10.8%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 22
 
10.8%
0 66
32.5%
1 54
26.6%
2 31
15.3%
3 27
13.3%
ValueCountFrequency (%)
3 27
13.3%
2 31
15.3%
1 54
26.6%
0 66
32.5%
-1 22
 
10.8%
-2 3
 
1.5%

carburant
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
essence
183 
diesel
20 

Length

Max length7
Median length7
Mean length6.9014778
Min length6

Characters and Unicode

Total characters1401
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowessence
2nd rowessence
3rd rowessence
4th rowessence
5th rowessence

Common Values

ValueCountFrequency (%)
essence 183
90.1%
diesel 20
 
9.9%

Length

2023-04-28T14:16:12.184383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:16:12.300643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
essence 183
90.1%
diesel 20
 
9.9%

Most occurring characters

ValueCountFrequency (%)
e 589
42.0%
s 386
27.6%
n 183
 
13.1%
c 183
 
13.1%
d 20
 
1.4%
i 20
 
1.4%
l 20
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1401
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 589
42.0%
s 386
27.6%
n 183
 
13.1%
c 183
 
13.1%
d 20
 
1.4%
i 20
 
1.4%
l 20
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1401
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 589
42.0%
s 386
27.6%
n 183
 
13.1%
c 183
 
13.1%
d 20
 
1.4%
i 20
 
1.4%
l 20
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1401
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 589
42.0%
s 386
27.6%
n 183
 
13.1%
c 183
 
13.1%
d 20
 
1.4%
i 20
 
1.4%
l 20
 
1.4%

turbo
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
atmosphérique
166 
turbo
37 

Length

Max length13
Median length13
Mean length11.541872
Min length5

Characters and Unicode

Total characters2343
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowatmosphérique
2nd rowatmosphérique
3rd rowatmosphérique
4th rowatmosphérique
5th rowatmosphérique

Common Values

ValueCountFrequency (%)
atmosphérique 166
81.8%
turbo 37
 
18.2%

Length

2023-04-28T14:16:12.396712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:16:12.512059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
atmosphérique 166
81.8%
turbo 37
 
18.2%

Most occurring characters

ValueCountFrequency (%)
t 203
 
8.7%
o 203
 
8.7%
r 203
 
8.7%
u 203
 
8.7%
a 166
 
7.1%
m 166
 
7.1%
s 166
 
7.1%
p 166
 
7.1%
h 166
 
7.1%
é 166
 
7.1%
Other values (4) 535
22.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2343
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 203
 
8.7%
o 203
 
8.7%
r 203
 
8.7%
u 203
 
8.7%
a 166
 
7.1%
m 166
 
7.1%
s 166
 
7.1%
p 166
 
7.1%
h 166
 
7.1%
é 166
 
7.1%
Other values (4) 535
22.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 2343
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 203
 
8.7%
o 203
 
8.7%
r 203
 
8.7%
u 203
 
8.7%
a 166
 
7.1%
m 166
 
7.1%
s 166
 
7.1%
p 166
 
7.1%
h 166
 
7.1%
é 166
 
7.1%
Other values (4) 535
22.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2177
92.9%
None 166
 
7.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 203
9.3%
o 203
9.3%
r 203
9.3%
u 203
9.3%
a 166
7.6%
m 166
7.6%
s 166
7.6%
p 166
7.6%
h 166
7.6%
i 166
7.6%
Other values (3) 369
16.9%
None
ValueCountFrequency (%)
é 166
100.0%

nombre_portes
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
quatre
114 
deux
89 

Length

Max length6
Median length6
Mean length5.1231527
Min length4

Characters and Unicode

Total characters1040
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdeux
2nd rowdeux
3rd rowdeux
4th rowquatre
5th rowquatre

Common Values

ValueCountFrequency (%)
quatre 114
56.2%
deux 89
43.8%

Length

2023-04-28T14:16:12.606072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:16:12.720890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
quatre 114
56.2%
deux 89
43.8%

Most occurring characters

ValueCountFrequency (%)
u 203
19.5%
e 203
19.5%
q 114
11.0%
a 114
11.0%
t 114
11.0%
r 114
11.0%
d 89
8.6%
x 89
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1040
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 203
19.5%
e 203
19.5%
q 114
11.0%
a 114
11.0%
t 114
11.0%
r 114
11.0%
d 89
8.6%
x 89
8.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 1040
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 203
19.5%
e 203
19.5%
q 114
11.0%
a 114
11.0%
t 114
11.0%
r 114
11.0%
d 89
8.6%
x 89
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 203
19.5%
e 203
19.5%
q 114
11.0%
a 114
11.0%
t 114
11.0%
r 114
11.0%
d 89
8.6%
x 89
8.6%

type_vehicule
Categorical

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
berline
95 
berline compacte
69 
break
25 
hardtop
 
8
convertible
 
6

Length

Max length16
Median length7
Mean length9.9310345
Min length5

Characters and Unicode

Total characters2016
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconvertible
2nd rowconvertible
3rd rowberline compacte
4th rowberline
5th rowberline

Common Values

ValueCountFrequency (%)
berline 95
46.8%
berline compacte 69
34.0%
break 25
 
12.3%
hardtop 8
 
3.9%
convertible 6
 
3.0%

Length

2023-04-28T14:16:12.810184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:16:12.927222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
berline 164
60.3%
compacte 69
25.4%
break 25
 
9.2%
hardtop 8
 
2.9%
convertible 6
 
2.2%

Most occurring characters

ValueCountFrequency (%)
e 434
21.5%
r 203
10.1%
b 195
9.7%
l 170
 
8.4%
i 170
 
8.4%
n 170
 
8.4%
c 144
 
7.1%
a 102
 
5.1%
t 83
 
4.1%
o 83
 
4.1%
Other values (7) 262
13.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1947
96.6%
Space Separator 69
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 434
22.3%
r 203
10.4%
b 195
10.0%
l 170
 
8.7%
i 170
 
8.7%
n 170
 
8.7%
c 144
 
7.4%
a 102
 
5.2%
t 83
 
4.3%
o 83
 
4.3%
Other values (6) 193
9.9%
Space Separator
ValueCountFrequency (%)
69
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1947
96.6%
Common 69
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 434
22.3%
r 203
10.4%
b 195
10.0%
l 170
 
8.7%
i 170
 
8.7%
n 170
 
8.7%
c 144
 
7.4%
a 102
 
5.2%
t 83
 
4.3%
o 83
 
4.3%
Other values (6) 193
9.9%
Common
ValueCountFrequency (%)
69
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 434
21.5%
r 203
10.1%
b 195
9.7%
l 170
 
8.4%
i 170
 
8.4%
n 170
 
8.4%
c 144
 
7.1%
a 102
 
5.1%
t 83
 
4.1%
o 83
 
4.1%
Other values (7) 262
13.0%

roues_motrices
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
traction
118 
propulsion
76 
quatre roues motrices
 
9

Length

Max length21
Median length8
Mean length9.3251232
Min length8

Characters and Unicode

Total characters1893
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpropulsion
2nd rowpropulsion
3rd rowpropulsion
4th rowtraction
5th rowquatre roues motrices

Common Values

ValueCountFrequency (%)
traction 118
58.1%
propulsion 76
37.4%
quatre roues motrices 9
 
4.4%

Length

2023-04-28T14:16:13.035729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:16:13.147823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
traction 118
53.4%
propulsion 76
34.4%
quatre 9
 
4.1%
roues 9
 
4.1%
motrices 9
 
4.1%

Most occurring characters

ValueCountFrequency (%)
o 288
15.2%
t 254
13.4%
r 221
11.7%
i 203
10.7%
n 194
10.2%
p 152
8.0%
a 127
6.7%
c 127
6.7%
u 94
 
5.0%
s 94
 
5.0%
Other values (5) 139
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1875
99.0%
Space Separator 18
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 288
15.4%
t 254
13.5%
r 221
11.8%
i 203
10.8%
n 194
10.3%
p 152
8.1%
a 127
6.8%
c 127
6.8%
u 94
 
5.0%
s 94
 
5.0%
Other values (4) 121
6.5%
Space Separator
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1875
99.0%
Common 18
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 288
15.4%
t 254
13.5%
r 221
11.8%
i 203
10.8%
n 194
10.3%
p 152
8.1%
a 127
6.8%
c 127
6.8%
u 94
 
5.0%
s 94
 
5.0%
Other values (4) 121
6.5%
Common
ValueCountFrequency (%)
18
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1893
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 288
15.2%
t 254
13.4%
r 221
11.7%
i 203
10.7%
n 194
10.2%
p 152
8.0%
a 127
6.7%
c 127
6.7%
u 94
 
5.0%
s 94
 
5.0%
Other values (5) 139
7.3%

emplacement_moteur
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
avant
200 
arrière
 
3

Length

Max length7
Median length5
Mean length5.0295567
Min length5

Characters and Unicode

Total characters1021
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowavant
2nd rowavant
3rd rowavant
4th rowavant
5th rowavant

Common Values

ValueCountFrequency (%)
avant 200
98.5%
arrière 3
 
1.5%

Length

2023-04-28T14:16:13.244746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:16:13.359312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
avant 200
98.5%
arrière 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
a 403
39.5%
v 200
19.6%
n 200
19.6%
t 200
19.6%
r 9
 
0.9%
i 3
 
0.3%
è 3
 
0.3%
e 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1021
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 403
39.5%
v 200
19.6%
n 200
19.6%
t 200
19.6%
r 9
 
0.9%
i 3
 
0.3%
è 3
 
0.3%
e 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1021
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 403
39.5%
v 200
19.6%
n 200
19.6%
t 200
19.6%
r 9
 
0.9%
i 3
 
0.3%
è 3
 
0.3%
e 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1018
99.7%
None 3
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 403
39.6%
v 200
19.6%
n 200
19.6%
t 200
19.6%
r 9
 
0.9%
i 3
 
0.3%
e 3
 
0.3%
None
ValueCountFrequency (%)
è 3
100.0%

empattement
Real number (ℝ)

Distinct53
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.789163
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-28T14:16:13.464999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93.01
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.0399643
Coefficient of variation (CV)0.061139949
Kurtosis0.9832542
Mean98.789163
Median Absolute Deviation (MAD)2.7
Skewness1.0378384
Sum20054.2
Variance36.481169
MonotonicityNot monotonic
2023-04-28T14:16:13.589087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.5 21
 
10.3%
93.7 19
 
9.4%
95.7 13
 
6.4%
96.5 8
 
3.9%
98.4 7
 
3.4%
97.3 7
 
3.4%
98.8 6
 
3.0%
96.3 6
 
3.0%
99.1 6
 
3.0%
107.9 6
 
3.0%
Other values (43) 104
51.2%
ValueCountFrequency (%)
86.6 2
 
1.0%
88.4 1
 
0.5%
88.6 2
 
1.0%
89.5 3
 
1.5%
91.3 2
 
1.0%
93 1
 
0.5%
93.1 5
 
2.5%
93.3 1
 
0.5%
93.7 19
9.4%
94.3 1
 
0.5%
ValueCountFrequency (%)
120.9 1
 
0.5%
115.6 2
 
1.0%
114.2 4
2.0%
113 2
 
1.0%
112 1
 
0.5%
110 3
1.5%
109.1 5
2.5%
108 1
 
0.5%
107.9 6
3.0%
106.7 1
 
0.5%

longueur
Real number (ℝ)

Distinct74
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.14384
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-28T14:16:13.724959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.3
Q1166.55
median173.2
Q3183.3
95-th percentile196.68
Maximum208.1
Range67
Interquartile range (IQR)16.75

Descriptive statistics

Standard deviation12.338152
Coefficient of variation (CV)0.07085035
Kurtosis-0.078004548
Mean174.14384
Median Absolute Deviation (MAD)6.9
Skewness0.14770384
Sum35351.2
Variance152.23
MonotonicityNot monotonic
2023-04-28T14:16:13.849993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.3 15
 
7.4%
188.8 11
 
5.4%
171.7 7
 
3.4%
186.7 7
 
3.4%
166.3 7
 
3.4%
165.3 6
 
3.0%
177.8 6
 
3.0%
176.2 6
 
3.0%
186.6 6
 
3.0%
176.8 5
 
2.5%
Other values (64) 127
62.6%
ValueCountFrequency (%)
141.1 1
 
0.5%
144.6 2
 
1.0%
150 3
 
1.5%
155.9 3
 
1.5%
157.1 1
 
0.5%
157.3 15
7.4%
157.9 1
 
0.5%
158.7 3
 
1.5%
158.8 1
 
0.5%
159.1 3
 
1.5%
ValueCountFrequency (%)
208.1 1
 
0.5%
202.6 2
1.0%
199.6 2
1.0%
199.2 1
 
0.5%
198.9 4
2.0%
197 1
 
0.5%
193.8 1
 
0.5%
192.7 3
1.5%
191.7 1
 
0.5%
190.9 2
1.0%

largeur
Real number (ℝ)

Distinct43
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.92266
Minimum60.3
Maximum72.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-28T14:16:13.980147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.1
median65.5
Q366.9
95-th percentile70.48
Maximum72.3
Range12
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1482157
Coefficient of variation (CV)0.032586908
Kurtosis0.68426717
Mean65.92266
Median Absolute Deviation (MAD)1.4
Skewness0.89627581
Sum13382.3
Variance4.6148305
MonotonicityNot monotonic
2023-04-28T14:16:14.107001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
63.8 24
 
11.8%
66.5 23
 
11.3%
65.4 14
 
6.9%
63.6 11
 
5.4%
68.4 10
 
4.9%
64.4 10
 
4.9%
64 9
 
4.4%
65.5 8
 
3.9%
65.2 7
 
3.4%
65.6 6
 
3.0%
Other values (33) 81
39.9%
ValueCountFrequency (%)
60.3 1
 
0.5%
61.8 1
 
0.5%
62.5 1
 
0.5%
63.6 11
5.4%
63.8 24
11.8%
63.9 3
 
1.5%
64 9
 
4.4%
64.1 2
 
1.0%
64.2 6
 
3.0%
64.4 10
4.9%
ValueCountFrequency (%)
72.3 1
 
0.5%
72 1
 
0.5%
71.7 3
1.5%
71.4 3
1.5%
70.9 1
 
0.5%
70.6 1
 
0.5%
70.5 1
 
0.5%
70.3 3
1.5%
69.6 2
1.0%
68.9 4
2.0%

hauteur
Real number (ℝ)

Distinct49
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.731034
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-28T14:16:14.234114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.4540676
Coefficient of variation (CV)0.045673188
Kurtosis-0.4635424
Mean53.731034
Median Absolute Deviation (MAD)1.6
Skewness0.055956321
Sum10907.4
Variance6.0224479
MonotonicityNot monotonic
2023-04-28T14:16:14.367194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.8 14
 
6.9%
55.7 12
 
5.9%
52 12
 
5.9%
54.5 10
 
4.9%
54.1 10
 
4.9%
55.5 9
 
4.4%
54.3 8
 
3.9%
56.7 8
 
3.9%
56.1 7
 
3.4%
52.6 7
 
3.4%
Other values (39) 106
52.2%
ValueCountFrequency (%)
47.8 1
 
0.5%
48.8 2
 
1.0%
49.4 2
 
1.0%
49.6 4
 
2.0%
49.7 3
 
1.5%
50.2 6
3.0%
50.5 2
 
1.0%
50.6 5
 
2.5%
50.8 14
6.9%
51 1
 
0.5%
ValueCountFrequency (%)
59.8 2
 
1.0%
59.1 3
 
1.5%
58.7 4
2.0%
58.3 1
 
0.5%
57.5 3
 
1.5%
56.7 8
3.9%
56.5 2
 
1.0%
56.3 2
 
1.0%
56.2 3
 
1.5%
56.1 7
3.4%

poids_vehicule
Real number (ℝ)

Distinct170
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2560.0788
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-28T14:16:14.502504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1900.5
Q12179.5
median2420
Q32943.5
95-th percentile3504
Maximum4066
Range2578
Interquartile range (IQR)764

Descriptive statistics

Standard deviation521.22148
Coefficient of variation (CV)0.20359587
Kurtosis-0.05577188
Mean2560.0788
Median Absolute Deviation (MAD)391
Skewness0.66684143
Sum519696
Variance271671.84
MonotonicityNot monotonic
2023-04-28T14:16:14.634326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2385 4
 
2.0%
1989 3
 
1.5%
1918 3
 
1.5%
2275 3
 
1.5%
2548 2
 
1.0%
2191 2
 
1.0%
2535 2
 
1.0%
2024 2
 
1.0%
2414 2
 
1.0%
4066 2
 
1.0%
Other values (160) 178
87.7%
ValueCountFrequency (%)
1488 1
0.5%
1713 1
0.5%
1819 1
0.5%
1837 1
0.5%
1874 2
1.0%
1876 2
1.0%
1889 1
0.5%
1890 1
0.5%
1900 1
0.5%
1905 1
0.5%
ValueCountFrequency (%)
4066 2
1.0%
3950 1
0.5%
3900 1
0.5%
3770 1
0.5%
3750 1
0.5%
3740 1
0.5%
3715 1
0.5%
3685 1
0.5%
3515 1
0.5%
3505 1
0.5%

type_moteur
Categorical

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
ohc
148 
ohcv
 
13
ohcf
 
13
dohc
 
12
l
 
12
Other values (2)
 
5

Length

Max length5
Median length3
Mean length3.1182266
Min length1

Characters and Unicode

Total characters633
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc 148
72.9%
ohcv 13
 
6.4%
ohcf 13
 
6.4%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Length

2023-04-28T14:16:14.750406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:16:14.881892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
ohc 148
72.9%
ohcv 13
 
6.4%
ohcf 13
 
6.4%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 195
30.8%
h 187
29.5%
c 187
29.5%
v 14
 
2.2%
f 13
 
2.1%
d 13
 
2.1%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 633
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 195
30.8%
h 187
29.5%
c 187
29.5%
v 14
 
2.2%
f 13
 
2.1%
d 13
 
2.1%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 633
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 195
30.8%
h 187
29.5%
c 187
29.5%
v 14
 
2.2%
f 13
 
2.1%
d 13
 
2.1%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 633
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 195
30.8%
h 187
29.5%
c 187
29.5%
v 14
 
2.2%
f 13
 
2.1%
d 13
 
2.1%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

nombre_cylindres
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
four
157 
six
24 
five
 
11
eight
 
5
two
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.9014778
Min length3

Characters and Unicode

Total characters792
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive

Common Values

ValueCountFrequency (%)
four 157
77.3%
six 24
 
11.8%
five 11
 
5.4%
eight 5
 
2.5%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Length

2023-04-28T14:16:14.989116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:16:15.117109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
four 157
77.3%
six 24
 
11.8%
five 11
 
5.4%
eight 5
 
2.5%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f 168
21.2%
o 161
20.3%
r 158
19.9%
u 157
19.8%
i 40
 
5.1%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 792
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 168
21.2%
o 161
20.3%
r 158
19.9%
u 157
19.8%
i 40
 
5.1%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 792
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 168
21.2%
o 161
20.3%
r 158
19.9%
u 157
19.8%
i 40
 
5.1%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 168
21.2%
o 161
20.3%
r 158
19.9%
u 157
19.8%
i 40
 
5.1%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

taille_moteur
Real number (ℝ)

Distinct44
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.14778
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-28T14:16:15.234613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197.5
median120
Q3143
95-th percentile202.1
Maximum326
Range265
Interquartile range (IQR)45.5

Descriptive statistics

Standard deviation41.773527
Coefficient of variation (CV)0.3285431
Kurtosis5.2380141
Mean127.14778
Median Absolute Deviation (MAD)23
Skewness1.9335616
Sum25811
Variance1745.0276
MonotonicityNot monotonic
2023-04-28T14:16:15.359729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
122 15
 
7.4%
92 15
 
7.4%
98 14
 
6.9%
97 13
 
6.4%
90 12
 
5.9%
108 12
 
5.9%
110 12
 
5.9%
109 8
 
3.9%
120 7
 
3.4%
141 7
 
3.4%
Other values (34) 88
43.3%
ValueCountFrequency (%)
61 1
 
0.5%
70 3
 
1.5%
79 1
 
0.5%
80 1
 
0.5%
90 12
5.9%
91 5
 
2.5%
92 15
7.4%
97 13
6.4%
98 14
6.9%
103 1
 
0.5%
ValueCountFrequency (%)
326 1
 
0.5%
308 1
 
0.5%
304 1
 
0.5%
258 2
 
1.0%
234 2
 
1.0%
209 3
1.5%
203 1
 
0.5%
194 3
1.5%
183 4
2.0%
181 6
3.0%
Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
mpfi
94 
2bbl
64 
idi
20 
1bbl
11 
spdi
 
9
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.8965517
Min length3

Characters and Unicode

Total characters791
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi 94
46.3%
2bbl 64
31.5%
idi 20
 
9.9%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Length

2023-04-28T14:16:15.472232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T14:16:15.597536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
mpfi 94
46.3%
2bbl 64
31.5%
idi 20
 
9.9%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
b 156
19.7%
i 145
18.3%
p 104
13.1%
f 96
12.1%
m 95
12.0%
l 78
9.9%
2 64
8.1%
d 29
 
3.7%
1 11
 
1.4%
s 10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 713
90.1%
Decimal Number 78
 
9.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 156
21.9%
i 145
20.3%
p 104
14.6%
f 96
13.5%
m 95
13.3%
l 78
10.9%
d 29
 
4.1%
s 10
 
1.4%
Decimal Number
ValueCountFrequency (%)
2 64
82.1%
1 11
 
14.1%
4 3
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 713
90.1%
Common 78
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 156
21.9%
i 145
20.3%
p 104
14.6%
f 96
13.5%
m 95
13.3%
l 78
10.9%
d 29
 
4.1%
s 10
 
1.4%
Common
ValueCountFrequency (%)
2 64
82.1%
1 11
 
14.1%
4 3
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 156
19.7%
i 145
18.3%
p 104
13.1%
f 96
12.1%
m 95
12.0%
l 78
9.9%
2 64
8.1%
d 29
 
3.7%
1 11
 
1.4%
s 10
 
1.3%

taux_alésage
Real number (ℝ)

Distinct38
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3268966
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-28T14:16:15.721634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.97
Q13.15
median3.31
Q33.58
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.27062949
Coefficient of variation (CV)0.081345929
Kurtosis-0.76767189
Mean3.3268966
Median Absolute Deviation (MAD)0.23
Skewness0.040086683
Sum675.36
Variance0.073240321
MonotonicityNot monotonic
2023-04-28T14:16:15.833909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3.62 21
 
10.3%
3.19 20
 
9.9%
3.15 15
 
7.4%
3.03 12
 
5.9%
2.97 12
 
5.9%
3.46 9
 
4.4%
3.31 8
 
3.9%
3.43 8
 
3.9%
3.78 8
 
3.9%
3.27 7
 
3.4%
Other values (28) 83
40.9%
ValueCountFrequency (%)
2.54 1
 
0.5%
2.68 1
 
0.5%
2.91 7
3.4%
2.92 1
 
0.5%
2.97 12
5.9%
2.99 1
 
0.5%
3.01 5
2.5%
3.03 12
5.9%
3.05 6
3.0%
3.08 1
 
0.5%
ValueCountFrequency (%)
3.94 2
 
1.0%
3.8 2
 
1.0%
3.78 8
 
3.9%
3.76 1
 
0.5%
3.74 3
 
1.5%
3.7 5
 
2.5%
3.63 2
 
1.0%
3.62 21
10.3%
3.61 1
 
0.5%
3.6 1
 
0.5%

course
Real number (ℝ)

Distinct36
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2628571
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-28T14:16:15.959996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.41
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.30564202
Coefficient of variation (CV)0.093673123
Kurtosis2.4002381
Mean3.2628571
Median Absolute Deviation (MAD)0.14
Skewness-0.65968543
Sum662.36
Variance0.093417044
MonotonicityNot monotonic
2023-04-28T14:16:16.231061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3.4 20
 
9.9%
3.15 14
 
6.9%
3.03 14
 
6.9%
3.23 14
 
6.9%
3.39 13
 
6.4%
2.64 10
 
4.9%
3.35 9
 
4.4%
3.29 9
 
4.4%
3.46 8
 
3.9%
3.07 6
 
3.0%
Other values (26) 86
42.4%
ValueCountFrequency (%)
2.07 1
 
0.5%
2.19 2
 
1.0%
2.64 10
4.9%
2.68 2
 
1.0%
2.76 1
 
0.5%
2.8 2
 
1.0%
2.87 1
 
0.5%
2.9 3
 
1.5%
3.03 14
6.9%
3.07 6
3.0%
ValueCountFrequency (%)
4.17 2
 
1.0%
3.9 3
 
1.5%
3.86 4
2.0%
3.64 5
2.5%
3.58 6
3.0%
3.54 4
2.0%
3.52 5
2.5%
3.5 6
3.0%
3.47 4
2.0%
3.46 8
3.9%

taux_compression
Real number (ℝ)

Distinct32
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.15133
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-28T14:16:16.346054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.55
median9
Q39.4
95-th percentile21.86
Maximum23
Range16
Interquartile range (IQR)0.85

Descriptive statistics

Standard deviation3.9905801
Coefficient of variation (CV)0.39310909
Kurtosis5.1392945
Mean10.15133
Median Absolute Deviation (MAD)0.4
Skewness2.5938477
Sum2060.72
Variance15.924729
MonotonicityNot monotonic
2023-04-28T14:16:16.449432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 45
22.2%
9.4 26
12.8%
8.5 14
 
6.9%
9.5 12
 
5.9%
9.3 11
 
5.4%
8.7 9
 
4.4%
8 8
 
3.9%
9.2 8
 
3.9%
7 7
 
3.4%
8.6 5
 
2.5%
Other values (22) 58
28.6%
ValueCountFrequency (%)
7 7
3.4%
7.5 5
 
2.5%
7.6 4
 
2.0%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
3.9%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.5%
8.5 14
6.9%
ValueCountFrequency (%)
23 5
2.5%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.0%
21 5
2.5%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 3
1.5%

chevaux
Real number (ℝ)

Distinct59
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.39901
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-28T14:16:16.572404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile181.4
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.631013
Coefficient of variation (CV)0.37961099
Kurtosis2.6470236
Mean104.39901
Median Absolute Deviation (MAD)25
Skewness1.3928436
Sum21193
Variance1570.6172
MonotonicityNot monotonic
2023-04-28T14:16:16.705991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
9.4%
70 11
 
5.4%
116 9
 
4.4%
69 9
 
4.4%
110 8
 
3.9%
95 7
 
3.4%
114 6
 
3.0%
160 6
 
3.0%
101 6
 
3.0%
62 6
 
3.0%
Other values (49) 116
57.1%
ValueCountFrequency (%)
48 1
 
0.5%
52 2
 
1.0%
55 1
 
0.5%
56 2
 
1.0%
58 1
 
0.5%
60 1
 
0.5%
62 6
 
3.0%
64 1
 
0.5%
68 19
9.4%
69 9
4.4%
ValueCountFrequency (%)
288 1
 
0.5%
262 1
 
0.5%
207 3
1.5%
200 1
 
0.5%
184 2
1.0%
182 3
1.5%
176 2
1.0%
175 1
 
0.5%
162 2
1.0%
161 2
1.0%

tour_moteur
Real number (ℝ)

Distinct22
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5127.8325
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-28T14:16:16.811526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5200
Q35500
95-th percentile5990
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation478.5252
Coefficient of variation (CV)0.093319195
Kurtosis0.074796639
Mean5127.8325
Median Absolute Deviation (MAD)300
Skewness0.060106102
Sum1040950
Variance228986.37
MonotonicityNot monotonic
2023-04-28T14:16:16.907362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
5500 37
18.2%
4800 35
17.2%
5000 27
13.3%
5200 23
11.3%
5400 13
 
6.4%
6000 9
 
4.4%
4500 7
 
3.4%
5800 7
 
3.4%
5250 7
 
3.4%
5100 5
 
2.5%
Other values (12) 33
16.3%
ValueCountFrequency (%)
4150 5
 
2.5%
4200 5
 
2.5%
4250 3
 
1.5%
4350 4
 
2.0%
4400 3
 
1.5%
4500 7
 
3.4%
4650 1
 
0.5%
4750 4
 
2.0%
4800 35
17.2%
5000 27
13.3%
ValueCountFrequency (%)
6600 2
 
1.0%
6000 9
 
4.4%
5900 3
 
1.5%
5800 7
 
3.4%
5750 1
 
0.5%
5600 1
 
0.5%
5500 37
18.2%
5400 13
 
6.4%
5300 1
 
0.5%
5250 7
 
3.4%

consommation_ville
Real number (ℝ)

Distinct28
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.157635
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-28T14:16:17.008756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.5441969
Coefficient of variation (CV)0.26012766
Kurtosis0.62760432
Mean25.157635
Median Absolute Deviation (MAD)5
Skewness0.68935098
Sum5107
Variance42.826513
MonotonicityNot monotonic
2023-04-28T14:16:17.110749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
31 27
13.3%
19 27
13.3%
24 22
10.8%
27 14
 
6.9%
17 13
 
6.4%
26 12
 
5.9%
23 12
 
5.9%
21 8
 
3.9%
25 8
 
3.9%
30 8
 
3.9%
Other values (18) 52
25.6%
ValueCountFrequency (%)
13 1
 
0.5%
14 2
 
1.0%
15 3
 
1.5%
16 6
 
3.0%
17 13
6.4%
18 3
 
1.5%
19 27
13.3%
20 3
 
1.5%
21 8
 
3.9%
22 4
 
2.0%
ValueCountFrequency (%)
49 1
 
0.5%
47 1
 
0.5%
45 1
 
0.5%
38 7
 
3.4%
37 6
 
3.0%
36 1
 
0.5%
35 1
 
0.5%
34 1
 
0.5%
33 1
 
0.5%
31 27
13.3%

consommation_autoroute
Real number (ℝ)

Distinct30
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.694581
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-28T14:16:17.225694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42.9
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8963558
Coefficient of variation (CV)0.22467665
Kurtosis0.46822061
Mean30.694581
Median Absolute Deviation (MAD)5
Skewness0.56200855
Sum6231
Variance47.559723
MonotonicityNot monotonic
2023-04-28T14:16:17.332035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25 19
 
9.4%
38 17
 
8.4%
24 17
 
8.4%
30 16
 
7.9%
32 16
 
7.9%
34 14
 
6.9%
28 13
 
6.4%
37 12
 
5.9%
29 10
 
4.9%
33 9
 
4.4%
Other values (20) 60
29.6%
ValueCountFrequency (%)
16 2
 
1.0%
17 1
 
0.5%
18 2
 
1.0%
19 2
 
1.0%
20 2
 
1.0%
22 8
3.9%
23 7
 
3.4%
24 17
8.4%
25 19
9.4%
26 3
 
1.5%
ValueCountFrequency (%)
54 1
 
0.5%
53 1
 
0.5%
50 1
 
0.5%
47 2
 
1.0%
46 2
 
1.0%
43 4
 
2.0%
42 3
 
1.5%
41 3
 
1.5%
39 2
 
1.0%
38 17
8.4%

prix
Real number (ℝ)

Distinct187
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13347.2
Minimum5151
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-04-28T14:16:17.463309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5151
5-th percentile6229
Q17847
median10345
Q316509
95-th percentile32500.2
Maximum45400
Range40249
Interquartile range (IQR)8662

Descriptive statistics

Standard deviation7995.7399
Coefficient of variation (CV)0.59905745
Kurtosis3.0206407
Mean13347.2
Median Absolute Deviation (MAD)3300
Skewness1.7716934
Sum2709481.7
Variance63931856
MonotonicityNot monotonic
2023-04-28T14:16:17.589335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8921 2
 
1.0%
18150 2
 
1.0%
7898 2
 
1.0%
8916.5 2
 
1.0%
7775 2
 
1.0%
8845 2
 
1.0%
7295 2
 
1.0%
7609 2
 
1.0%
6692 2
 
1.0%
6229 2
 
1.0%
Other values (177) 183
90.1%
ValueCountFrequency (%)
5151 1
0.5%
5195 1
0.5%
5348 1
0.5%
5389 1
0.5%
5399 1
0.5%
5499 1
0.5%
5572 2
1.0%
6095 1
0.5%
6189 1
0.5%
6229 2
1.0%
ValueCountFrequency (%)
45400 1
0.5%
41315 1
0.5%
40960 1
0.5%
37028 1
0.5%
36880 1
0.5%
36000 1
0.5%
35550 1
0.5%
35056 1
0.5%
34184 1
0.5%
34028 1
0.5%

marque
Categorical

Distinct28
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
toyota
31 
nissan
17 
mazda
15 
mitsubishi
13 
honda
13 
Other values (23)
114 

Length

Max length11
Median length10
Mean length6.1477833
Min length2

Characters and Unicode

Total characters1248
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)2.5%

Sample

1st rowalfa-romero
2nd rowalfa-romero
3rd rowalfa-romero
4th rowaudi
5th rowaudi

Common Values

ValueCountFrequency (%)
toyota 31
15.3%
nissan 17
 
8.4%
mazda 15
 
7.4%
mitsubishi 13
 
6.4%
honda 13
 
6.4%
peugeot 11
 
5.4%
volvo 11
 
5.4%
subaru 10
 
4.9%
volkswagen 9
 
4.4%
dodge 9
 
4.4%
Other values (18) 64
31.5%

Length

2023-04-28T14:16:17.712641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 31
15.3%
nissan 18
 
8.9%
mazda 15
 
7.4%
mitsubishi 13
 
6.4%
honda 13
 
6.4%
peugeot 11
 
5.4%
volvo 11
 
5.4%
subaru 10
 
4.9%
volkswagen 9
 
4.4%
dodge 9
 
4.4%
Other values (17) 63
31.0%

Most occurring characters

ValueCountFrequency (%)
o 150
12.0%
a 150
12.0%
t 100
 
8.0%
s 97
 
7.8%
u 81
 
6.5%
i 76
 
6.1%
n 60
 
4.8%
e 58
 
4.6%
d 55
 
4.4%
m 49
 
3.9%
Other values (17) 372
29.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1244
99.7%
Dash Punctuation 3
 
0.2%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 150
12.1%
a 150
12.1%
t 100
 
8.0%
s 97
 
7.8%
u 81
 
6.5%
i 76
 
6.1%
n 60
 
4.8%
e 58
 
4.7%
d 55
 
4.4%
m 49
 
3.9%
Other values (15) 368
29.6%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1245
99.8%
Common 3
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 150
12.0%
a 150
12.0%
t 100
 
8.0%
s 97
 
7.8%
u 81
 
6.5%
i 76
 
6.1%
n 60
 
4.8%
e 58
 
4.7%
d 55
 
4.4%
m 49
 
3.9%
Other values (16) 369
29.6%
Common
ValueCountFrequency (%)
- 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1248
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 150
12.0%
a 150
12.0%
t 100
 
8.0%
s 97
 
7.8%
u 81
 
6.5%
i 76
 
6.1%
n 60
 
4.8%
e 58
 
4.6%
d 55
 
4.4%
m 49
 
3.9%
Other values (17) 372
29.8%

modèle
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct141
Distinct (%)69.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
corolla
 
6
corona
 
6
504
 
6
dl
 
4
civic
 
3
Other values (136)
178 

Length

Max length25
Median length18
Mean length7.0788177
Min length2

Characters and Unicode

Total characters1437
Distinct characters45
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique102 ?
Unique (%)50.2%

Sample

1st rowgiulia
2nd rowstelvio
3rd rowQuadrifoglio
4th row100 ls
5th row100ls

Common Values

ValueCountFrequency (%)
corolla 6
 
3.0%
corona 6
 
3.0%
504 6
 
3.0%
dl 4
 
2.0%
civic 3
 
1.5%
mirage g4 3
 
1.5%
mark ii 3
 
1.5%
g4 3
 
1.5%
rabbit 3
 
1.5%
outlander 3
 
1.5%
Other values (131) 163
80.3%

Length

2023-04-28T14:16:17.819355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
corolla 12
 
4.2%
sw 10
 
3.5%
corona 9
 
3.2%
glc 8
 
2.8%
custom 8
 
2.8%
civic 8
 
2.8%
504 7
 
2.5%
g4 6
 
2.1%
deluxe 5
 
1.8%
mirage 4
 
1.4%
Other values (141) 206
72.8%

Most occurring characters

ValueCountFrequency (%)
c 108
 
7.5%
a 107
 
7.4%
l 103
 
7.2%
r 100
 
7.0%
e 100
 
7.0%
o 93
 
6.5%
82
 
5.7%
i 71
 
4.9%
t 67
 
4.7%
s 54
 
3.8%
Other values (35) 552
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1128
78.5%
Decimal Number 179
 
12.5%
Space Separator 82
 
5.7%
Close Punctuation 13
 
0.9%
Open Punctuation 13
 
0.9%
Uppercase Letter 12
 
0.8%
Dash Punctuation 10
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 108
 
9.6%
a 107
 
9.5%
l 103
 
9.1%
r 100
 
8.9%
e 100
 
8.9%
o 93
 
8.2%
i 71
 
6.3%
t 67
 
5.9%
s 54
 
4.8%
u 41
 
3.6%
Other values (15) 284
25.2%
Decimal Number
ValueCountFrequency (%)
0 44
24.6%
4 37
20.7%
1 23
12.8%
2 21
11.7%
5 18
10.1%
9 12
 
6.7%
6 12
 
6.7%
3 10
 
5.6%
7 2
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
M 4
33.3%
D 3
25.0%
U 1
 
8.3%
X 1
 
8.3%
Q 1
 
8.3%
V 1
 
8.3%
C 1
 
8.3%
Space Separator
ValueCountFrequency (%)
82
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1140
79.3%
Common 297
 
20.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 108
 
9.5%
a 107
 
9.4%
l 103
 
9.0%
r 100
 
8.8%
e 100
 
8.8%
o 93
 
8.2%
i 71
 
6.2%
t 67
 
5.9%
s 54
 
4.7%
u 41
 
3.6%
Other values (22) 296
26.0%
Common
ValueCountFrequency (%)
82
27.6%
0 44
14.8%
4 37
12.5%
1 23
 
7.7%
2 21
 
7.1%
5 18
 
6.1%
) 13
 
4.4%
( 13
 
4.4%
9 12
 
4.0%
6 12
 
4.0%
Other values (3) 22
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1437
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 108
 
7.5%
a 107
 
7.4%
l 103
 
7.2%
r 100
 
7.0%
e 100
 
7.0%
o 93
 
6.5%
82
 
5.7%
i 71
 
4.9%
t 67
 
4.7%
s 54
 
3.8%
Other values (35) 552
38.4%

Interactions

2023-04-28T14:16:09.584906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:15:49.072094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:15:50.608724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:15:52.085398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:15:53.539756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:15:55.085065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:15:56.479426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:15:57.895621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:15:59.295440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:16:00.903275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:16:02.272117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:16:03.738462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:16:05.103776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-28T14:15:51.979899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:15:53.433634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:15:54.984382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:15:56.377640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:15:57.792092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:15:59.193676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:16:00.794647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:16:02.176442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:16:03.634571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:16:05.004292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:16:06.528103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:16:07.984287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-28T14:16:09.478330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-28T14:16:17.940131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
etat_de_routeempattementlongueurlargeurhauteurpoids_vehiculetaille_moteurtaux_alésagecoursetaux_compressionchevauxtour_moteurconsommation_villeconsommation_autorouteprixcarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurtype_moteurnombre_cylindressysteme_carburantmarque
etat_de_route1.000-0.537-0.394-0.250-0.527-0.258-0.175-0.173-0.0160.029-0.0060.283-0.0190.053-0.1430.2190.1810.6820.3340.2660.2710.2210.1600.2690.445
empattement-0.5371.0000.9120.8110.6360.7660.6460.5510.221-0.1310.501-0.317-0.493-0.5380.6820.3400.3090.4440.3320.4170.5680.3550.3160.2260.510
longueur-0.3940.9121.0000.8870.5290.8910.7800.6580.176-0.1940.657-0.277-0.669-0.6970.8040.1040.2060.3620.2390.4090.0000.3150.3560.3260.490
largeur-0.2500.8110.8871.0000.3530.8650.7700.6300.230-0.1490.686-0.207-0.688-0.7010.8110.2310.3000.3040.1240.4020.1590.3680.5670.2460.515
hauteur-0.5270.6360.5290.3531.0000.3460.1980.222-0.0240.0040.008-0.300-0.064-0.1290.2410.2750.2320.5450.4980.3610.2820.3840.3500.2910.460
poids_vehicule-0.2580.7660.8910.8650.3461.0000.8770.7200.150-0.2160.806-0.245-0.811-0.8320.9090.3030.3740.2760.2300.4540.0970.3250.4820.2900.486
taille_moteur-0.1750.6460.7800.7700.1980.8771.0000.7160.284-0.2330.815-0.281-0.729-0.7190.8260.1550.2680.2080.2010.4670.6180.5270.6420.3310.513
taux_alésage-0.1730.5510.6580.6300.2220.7200.7161.000-0.065-0.1700.655-0.294-0.633-0.6320.6720.1670.3460.1640.1540.4410.3260.4120.2580.3480.528
course-0.0160.2210.1760.230-0.0240.1500.284-0.0651.000-0.0660.117-0.087-0.010-0.0140.0900.3800.2710.1200.1650.3560.6180.4040.2480.3080.604
taux_compression0.029-0.131-0.194-0.1490.004-0.216-0.233-0.170-0.0661.000-0.353-0.0150.4770.443-0.1720.9930.5530.1870.0470.1110.0000.3360.5210.5180.483
chevaux-0.0060.5010.6570.6860.0080.8060.8150.6550.117-0.3531.0000.107-0.911-0.8840.8540.2210.3410.1640.1880.4000.8430.5180.5640.3170.453
tour_moteur0.283-0.317-0.277-0.207-0.300-0.245-0.281-0.294-0.087-0.0150.1071.000-0.122-0.050-0.0770.5940.3100.2390.0710.2460.4470.3620.2820.3630.463
consommation_ville-0.019-0.493-0.669-0.688-0.064-0.811-0.729-0.633-0.0100.477-0.911-0.1221.0000.968-0.8280.4330.1950.0000.0000.3770.1080.2040.4230.3110.341
consommation_autoroute0.053-0.538-0.697-0.701-0.129-0.832-0.719-0.632-0.0140.443-0.884-0.0500.9681.000-0.8220.3350.3160.1200.0000.4340.0980.3290.5000.3380.403
prix-0.1430.6820.8040.8110.2410.9090.8260.6720.090-0.1720.854-0.077-0.828-0.8221.0000.3290.4040.0000.2290.4440.4500.2850.4300.2870.369
carburant0.2190.3400.1040.2310.2750.3030.1550.1670.3800.9930.2210.5940.4330.3350.3291.0000.3730.1610.1740.0850.0000.2470.1540.9850.381
turbo0.1810.3090.2060.3000.2320.3740.2680.3460.2710.5530.3410.3100.1950.3160.4040.3731.0000.0000.0000.1140.0000.1460.1960.6090.377
nombre_portes0.6820.4440.3620.3040.5450.2760.2080.1640.1200.1870.1640.2390.0000.1200.0000.1610.0001.0000.7390.0510.0680.2020.1350.2460.344
type_vehicule0.3340.3320.2390.1240.4980.2300.2010.1540.1650.0470.1880.0710.0000.0000.2290.1740.0000.7391.0000.2120.4380.1450.0670.1440.356
roues_motrices0.2660.4170.4090.4020.3610.4540.4670.4410.3560.1110.4000.2460.3770.4340.4440.0850.1140.0510.2121.0000.1230.4420.3340.3850.602
emplacement_moteur0.2710.5680.0000.1590.2820.0970.6180.3260.6180.0000.8430.4470.1080.0980.4500.0000.0000.0680.4380.1231.0000.4360.2870.0000.728
type_moteur0.2210.3550.3150.3680.3840.3250.5270.4120.4040.3360.5180.3620.2040.3290.2850.2470.1460.2020.1450.4420.4361.0000.5460.3750.625
nombre_cylindres0.1600.3160.3560.5670.3500.4820.6420.2580.2480.5210.5640.2820.4230.5000.4300.1540.1960.1350.0670.3340.2870.5461.0000.3730.530
systeme_carburant0.2690.2260.3260.2460.2910.2900.3310.3480.3080.5180.3170.3630.3110.3380.2870.9850.6090.2460.1440.3850.0000.3750.3731.0000.490
marque0.4450.5100.4900.5150.4600.4860.5130.5280.6040.4830.4530.4630.3410.4030.3690.3810.3770.3440.3560.6020.7280.6250.5300.4901.000

Missing values

2023-04-28T14:16:11.256273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-28T14:16:11.787612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

etat_de_routecarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurempattementlongueurlargeurhauteurpoids_vehiculetype_moteurnombre_cylindrestaille_moteursysteme_carburanttaux_alésagecoursetaux_compressionchevauxtour_moteurconsommation_villeconsommation_autorouteprixmarquemodèle
03essenceatmosphériquedeuxconvertiblepropulsionavant88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212713495.000alfa-romerogiulia
13essenceatmosphériquedeuxconvertiblepropulsionavant88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212716500.000alfa-romerostelvio
21essenceatmosphériquedeuxberline compactepropulsionavant94.5171.265.552.42823ohcvsix152mpfi2.683.479.01545000192616500.000alfa-romeroQuadrifoglio
32essenceatmosphériquequatreberlinetractionavant99.8176.666.254.32337ohcfour109mpfi3.193.4010.01025500243013950.000audi100 ls
42essenceatmosphériquequatreberlinequatre roues motricesavant99.4176.666.454.32824ohcfive136mpfi3.193.408.01155500182217450.000audi100ls
52essenceatmosphériquedeuxberlinetractionavant99.8177.366.353.12507ohcfive136mpfi3.193.408.51105500192515250.000audifox
61essenceatmosphériquequatreberlinetractionavant105.8192.771.455.72844ohcfive136mpfi3.193.408.51105500192517710.000audi100ls
71essenceatmosphériquequatrebreaktractionavant105.8192.771.455.72954ohcfive136mpfi3.193.408.51105500192518920.000audi5000
81essenceturboquatreberlinetractionavant105.8192.771.455.93086ohcfive131mpfi3.133.408.31405500172023875.000audi4000
90essenceturbodeuxberline compactequatre roues motricesavant99.5178.267.952.03053ohcfive131mpfi3.133.407.01605500162217859.167audi5000s (diesel)
etat_de_routecarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurempattementlongueurlargeurhauteurpoids_vehiculetype_moteurnombre_cylindrestaille_moteursysteme_carburanttaux_alésagecoursetaux_compressionchevauxtour_moteurconsommation_villeconsommation_autorouteprixmarquemodèle
195-1essenceatmosphériquequatrebreakpropulsionavant104.3188.867.257.53034ohcfour141mpfi3.783.159.51145400232813415.0volvo144ea
196-2essenceatmosphériquequatreberlinepropulsionavant104.3188.867.256.22935ohcfour141mpfi3.783.159.51145400242815985.0volvo244dl
197-1essenceatmosphériquequatrebreakpropulsionavant104.3188.867.257.53042ohcfour141mpfi3.783.159.51145400242816515.0volvo245
198-2essenceturboquatreberlinepropulsionavant104.3188.867.256.23045ohcfour130mpfi3.623.157.51625100172218420.0volvo264gl
199-1essenceturboquatrebreakpropulsionavant104.3188.867.257.53157ohcfour130mpfi3.623.157.51625100172218950.0volvodiesel
200-1essenceatmosphériquequatreberlinepropulsionavant109.1188.868.955.52952ohcfour141mpfi3.783.159.51145400232816845.0volvo145e (sw)
201-1essenceturboquatreberlinepropulsionavant109.1188.868.855.53049ohcfour141mpfi3.783.158.71605300192519045.0volvo144ea
202-1essenceatmosphériquequatreberlinepropulsionavant109.1188.868.955.53012ohcvsix173mpfi3.582.878.81345500182321485.0volvo244dl
203-1dieselturboquatreberlinepropulsionavant109.1188.868.955.53217ohcsix145idi3.013.4023.01064800262722470.0volvo246
204-1essenceturboquatreberlinepropulsionavant109.1188.868.955.53062ohcfour141mpfi3.783.159.51145400192522625.0volvo264gl